Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …
smart monitoring and decision-making solutions. Near real-time and online damage …
[HTML][HTML] The state of the art of data science and engineering in structural health monitoring
Structural health monitoring (SHM) is a multi-discipline field that involves the automatic
sensing of structural loads and response by means of a large number of sensors and …
sensing of structural loads and response by means of a large number of sensors and …
Review of bridge structural health monitoring aided by big data and artificial intelligence: From condition assessment to damage detection
Structural health monitoring (SHM) techniques have been widely used in long-span bridges.
However, due to limitations of computational ability and data analysis methods, the …
However, due to limitations of computational ability and data analysis methods, the …
Wasserstein regression
The analysis of samples of random objects that do not lie in a vector space is gaining
increasing attention in statistics. An important class of such object data is univariate …
increasing attention in statistics. An important class of such object data is univariate …
High-dimensional data analytics in structural health monitoring and non-destructive evaluation: A review paper
H Momeni, A Ebrahimkhanlou - Smart Materials and Structures, 2022 - iopscience.iop.org
This paper aims to review high-dimensional data analytic (HDDA) methods for structural
health monitoring (SHM) and non-destructive evaluation (NDE) applications. High …
health monitoring (SHM) and non-destructive evaluation (NDE) applications. High …
Modeling probability density functions as data objects
Recent developments in the probabilistic and statistical analysis of probability density
functions are reviewed. Density functions are treated as data objects for which suitable …
functions are reviewed. Density functions are treated as data objects for which suitable …
Missing measurement data recovery methods in structural health monitoring: The state, challenges and case study
In the field of structural health monitoring (SHM), the sensor measurement signals collected
from the structure are the foundation and key of the SHM system. However, the loss of …
from the structure are the foundation and key of the SHM system. However, the loss of …
A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems
Missing data, especially a block of missing data, inevitably occur in structural health
monitoring systems. Because of their severe negative effects, many methods that use …
monitoring systems. Because of their severe negative effects, many methods that use …
Damage identification of long-span bridges based on the correlation of probability distribution of monitored quasi-static responses
Structural health diagnosis is one of the most critical issues in structural health monitoring
(SHM). Vibration-based methods have been extensively investigated in the last few decades …
(SHM). Vibration-based methods have been extensively investigated in the last few decades …
A two‐stage data cleansing method for bridge global positioning system monitoring data based on bi‐direction long and short term memory anomaly identification and …
K Yang, Y Ding, H Jiang, H Zhao… - Structural Control and …, 2022 - Wiley Online Library
Data cleansing is an essential approach for improving data quality. Therefore, it is the key to
avoiding the false alarm of the monitoring system due to the anomaly of the data itself. Data …
avoiding the false alarm of the monitoring system due to the anomaly of the data itself. Data …